Fairness in PCA-Based Recommenders

Predicting what someone will want next is harder than it looks. We explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enterprise and Society. David shares insights from his research on how machine learning models can inadvertently create unfairness, particularly for minority and niche user groups, even without any malicious intent.

Guest

David Liu: David Liu is an Assistant Research Professor at Cornell University's Center for Data Science for Enterprise and Society where he examines how AI impacts society. Specifically, he is interested in 1) understanding how machine learning models homogenize heterogeneous populations and 2) building models that better capture the unique preferences and identities of marginalized individuals. Prior to Cornell, David obtained a Ph.D. in Computer Science from Northeastern University, with the support of the NSF GRFP, and a B.S.E. from Princeton University. He has worked in industry as a research-scientist intern at Meta, a sociotechnical researcher at Taraaz, and a software engineer at Bloomberg LP.